Monday, 30th September 2013: Business & policy Dialogue

Tuesday 1 October to Thursday, 3rd October: Academic and Policy Dialogue

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ENDORSING PARTNERS

The following are confirmed contributors to the business and policy dialogue in Sydney:

• Rick Sawers (National Australia Bank)

• Nick Greiner (Chairman (Infrastructure NSW)

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The roles of evolutionary computation, fitness landscape,

constructive methods and local searches in the development of adaptive systems

for infrastructure planning

Presented by: Mr Mehrdad Amirghasemi, SMART Infrastructure Facility, University of Wollongong

The Roles of Evolutionary Computation, Fitness Landscape, Constructive Methods and Local Searches in the Development of Adaptive Systems for Infrastructure Planning

Mehrdad Amirghasemi Reza Zamani

The roles of evolutionary computation, fitness landscape, constructive methods and local searches in the development

of adaptive systems for infrastructure planning

Outline • Introduction

– Infrastructure problems. • Simple representative problems. • Hybrid model description. • Benchmark test results.

Infrastructure • Connected elements which are structurally related and

each element affects other elements. • Involved with the optimized selection of a set of values,

among a set of alternatives, for a number of variables. • Interaction among variables makes these problems

extremely hard. • Sophisticated optimization techniques and mathematical

programming are the key issues in reaching a reasonable level of efficiency in solving infrastructure problems.

Infrastructure problems difficulty

Z

Y

X

Time H

orizo

n

Prob

abili

stic

Nat

ure

Number of Variables

(1,1,1)

(0,0,0)

Simplified problem: minimizing traffic

A B C D E

1 4 2 3 5

A B C D E

5 1 3 2 4

A

BC

DE

distance

Avg. traffic

Library(1)

Hospital (2)

Sport Centre (3)Shopping Centre (4)

University (5)

π1 π2

The complexity of the problems • Simplified mathematical model:

Traffic_Volume(𝜋) = ∑ ∑ 𝑓𝑖𝑖𝑑𝜋 𝑖 𝜋(𝑖)𝑛𝑖=1 𝑛

𝑖=1 • The problem is called Quadratic Assignment Problem(QAP). • As the problem size increases linearly, the size of solution space

increases exponentially. • 5!=5×4×3×2×1=120 • 60! = 60×59× … ×2×1 = 8×1081 • With an 8 Ghz processor, evaluating all possibilities takes 1066

years of computation time. The age of the universe is around 1010 years.

Speed of computers • This “intractability” is despite the fact that the computer

industry has progressed so fast.

“If the car industry moved as fast as the computer industry, cars would get 470, 000 mph, 100,000 miles per gallon, and would cost three cents.” –Paul Otellini, Intel CEO.

• This necessitates a need for better algorithms that are capable of yielding high-quality solutions in a reasonable amount of time.

The proposed hybrid meta-heuristic • The aim is to develop effective

problem-solving procedures to obtain high quality solutions fast.

• Generally the designed algorithms consist of four modules: – A constructive procedure to produce

a pool of high quality initial solutions.

– A Local Search procedure to improve a given, complete solution.

– A population based (evolutionary) module to combine solutions from the current pool.

– A Synchronizer module, which facilitate interaction among the above three modules.

Constructive Method

Local Search

Synchronizer

Genetic Algotihm

Fitness landscape • When solved with local search methods, the difficulty of an

optimization problem is directly related to the shape of its fitness landscape.

The fitness landscape of an easy optimization problem

The fitness landscape of a hard optimization problem

Modular design • Based on “No-free-lunch” theorem

(Wolpert and Macready 1997), incorporating problem-specific knowledge, and matching the “procedure” with the “problem” is essential for developing high performance procedures.

• The ideal case is to have a generic procedure to handle similar problems.

• A modular design helps to achieve a balance between the above two facts.

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Benchmark test results • Designed Algorithms have been applied to three well-

known problems in the literature: – Quadratic Assignment, QAP – Job Shop Problem, JSP – Permutation Flow Shop Problem, PFSP

• Competitive results have been achieved on all benchmark tests.

• For JSP, a notorious instance is solved 6 times faster the fastest available method in the literature.

References Boese, K. D., A. B. Kahng, et al. (1994). "A new adaptive multi-start technique for combinatorial global optimizations." Operations Research Letters 16(2): 101-113.

De Jong, K. A. (2006). Evolutionary computation: a unified approach, MIT Press.

Fogel, D. B. (1994). "An introduction to simulated evolutionary optimization." Neural Networks, IEEE Transactions on 5(1): 3-14.

Jones, T. and S. Forrest (1995). Fitness Distance Correlation as a Measure of Problem Difficulty for Genetic Algorithms. Proceedings of the 6th International Conference on Genetic Algorithms, Morgan Kaufmann Publishers Inc.: 184-192.

Wolpert, D. H. and W. G. Macready (1997). "No free lunch theorems for optimization." Evolutionary Computation, IEEE Transactions on 1(1): 67-82.

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Mehrdad Amirghasemi PhD student ma604 (at) uow.edu.au

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